cn.MOPS is a model-based algorithm used to quantitatively detect copy-number variations in next-generation, DNA-sequencing data. The algorithm is implemented as an R package and can speed up processing with multi-CPU parallelism. However, the maximum achievable speedup is limited by the overhead of multi-CPU parallelism, which increases with the number of CPU cores used. In this thesis, an alternative mechanism of process acceleration is proposed. Using one CPU core and a GPU device, the proposed solution, gcn.MOPS, achieved a speedup factor of 159× and decreased memory usage by more than half. This speedup was substantially higher than the maximum achievable speedup in cn.MOPS, which was ∼20×. / Graduate / 0984 / 0544 / 0715 / alkhamis@uvic.ca
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/8286 |
| Date | 16 June 2017 |
| Creators | Alkhamis, Mohammad |
| Contributors | Baniasadi, Amirali |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web, http://creativecommons.org/licenses/by-nc-sa/2.5/ca/ |
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